SFARI Genes for All Lobes but Occipital

#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')

library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(plotlyutils)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(Rtsne)
library(ClusterR)
library(DESeq2)
library(expss)
library(knitr)

Load preprocessed dataset (preprocessing code in 20_03_03_data_preprocessing.Rmd)

# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame

# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>% 
              mutate('ID'=as.character(ensembl_gene_id)) %>% 
              dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
              mutate('Neuronal'=1)

# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_with_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]

# Update DE_info with SFARI and Neuronal information
DE_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>% 
  mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
  distinct(ID, .keep_all = TRUE) %>% left_join(GO_neuronal, by='ID') %>%
  mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
  mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`), significant=padj<0.05 & !is.na(padj))


SFARI_colour_hue = function(r) {
  pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}

---

SFARI Gene list

cat(paste0('There are ', length(unique(SFARI_genes$`gene-symbol`)), ' genes with a SFARI score'))

## There are 979 genes with a SFARI score

The results from this section don’t change depending on the brain region analised, so I’m not going to repeat them here. These can be found in the folder where all the brain regions were analysed together

---

Exploratory Analysis

As in the previous section, the results from this section don’t change depending on the brain region analised, so I’m not going to repeat them here. These can be found in the folder where all the brain regions were analysed together

Gene Expression

Normalised data

• The higher the SFARI score, the higher the mean expression of the gene: This pattern is quite strong and it doesn’t have any biological interpretation, so it’s probably bias in the SFARI score assignment

• The higher the SFARI score, the lower the standard deviation: This pattern is not as strong, but it is weird because the data was originally heteroscedastic with a positive relation between mean and variance, so the fact that the relation now seems to have reversed could mean that the vst normalisation ended up affecting the highly expressed genes more than it should have when trying to correct their higher variance

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr), 'SDExpr'=apply(datExpr,1,sd)) %>% 
            left_join(DE_info, by='ID')

p1 = ggplotly(plot_data %>% ggplot(aes(gene.score, MeanExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              theme(legend.position='none'))

p2 = ggplotly(plot_data %>% ggplot(aes(gene.score, SDExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              ggtitle('Mean Expression (left) and SD (right) by SFARI score') + 
              theme(legend.position='none'))

subplot(p1, p2, nrows=1)

rm(plot_data, p1, p2)

Raw data

Just to corroborate that the relation between sd and SFARI score used to be in the opposite direction before the normalisation: The higher the SFARI score the higher the mean expression and the higher the standard deviation

*There are a lot of outliers, but the plot is interactive so you can zoom in

# Save preprocessed results
datExpr_prep = datExpr
datMeta_prep = datMeta
DE_info_prep = DE_info

load('./../Data/filtered_raw_data.RData')

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr), 'SDExpr'=apply(datExpr,1,sd)) %>% 
            left_join(DE_info, by='ID')

p1 = ggplotly(plot_data %>% ggplot(aes(gene.score, MeanExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              theme(legend.position='none'))

p2 = ggplotly(plot_data %>% ggplot(aes(gene.score, SDExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              ggtitle('Mean Expression (left) and SD (right) by SFARI score') + 
              theme(legend.position='none'))

subplot(p1, p2, nrows=1)

rm(plot_data, p1, p2)

Return to normalised version of the data

# Save preprocessed results
datExpr = datExpr_prep
datMeta = datMeta_prep
DE_info = DE_info_prep

rm(datExpr_prep, datMeta_prep, DE_info_prep)

Log Fold Change

There seems to be a negative relation between SFARI score and log fold change when it would be expected to be either positively correlated or independent from each other (this last one because there are other factors that determine if a gene is releated to Autism apart from differences in gene expression)

Wikipedia mentions the likely explanation for this: “A disadvantage and serious risk of using fold change in this setting is that it is biased and may misclassify differentially expressed genes with large differences (B − A) but small ratios (B/A), leading to poor identification of changes at high expression levels”.

Based on this, since we saw there is a strong relation between SFARI score and mean expression, the bias in log fold change affects mainly genes with high SFARI scores, which would be the ones we are most interested in.

On top of this, I believe this effect is made more extreme by the pattern found in the previous plots, since the higher expressed genes were the most affected by the normalisation transformation, ending up with a smaller variance than the rest of the data, which is related to smaller ratios. (This is a constant problem independently of the normalisation function used).

ggplotly(DE_info %>% ggplot(aes(x=gene.score, y=abs(log2FoldChange), fill=gene.score)) + 
         geom_boxplot() + scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + 
         theme_minimal() + theme(legend.position='none'))

Effects of modifying filtering threshold by SFARI score

• The behaviour is very similar to the one found when considering all of the brain regions

• All SFARI scores except 6 are more affected by the filtering than the genes with Neuronal-related functional annotations

• SFARI score 1 is the one with the consistently lowest percentage of remaining genes

• Using the null hypothesis \(H0: lfc=0\), 381/907 SFARI genes are statistically significant (~42%)

lfc_list = seq(1, 1.25, 0.01)

all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_info)))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_info$Neuronal)))
lfc_counts_all = DE_info %>% group_by(`gene-score`) %>% tally %>%
                 mutate('group'=as.factor(`gene-score`), 'n'=as.character(n)) %>%
                 dplyr::select(group, n) %>%
                 bind_rows(Neuronal_counts, all_counts) %>%
                 mutate('lfc'=-1) %>%  dplyr::select(lfc, group, n)

for(lfc in lfc_list){
  
  # Recalculate DE_info with the new threshold (p-values change)
  DE_genes = results(dds, lfcThreshold=log2(lfc), altHypothesis='greaterAbs') %>% data.frame
  
  DE_genes = DE_genes %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>% 
             mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
             distinct(ID, .keep_all = TRUE) %>% left_join(GO_neuronal, by='ID') %>%
             mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
             mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`))
  
  DE_genes = DE_genes %>% filter(padj<0.05 & abs(log2FoldChange)>log2(lfc))

  
  # Calculate counts by groups
  all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_genes)))
  Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_genes$Neuronal)))
  lfc_counts = DE_genes %>% group_by(`gene-score`) %>% tally %>%
               mutate('group'=`gene-score`, 'n'=as.character(n)) %>%
               bind_rows(Neuronal_counts, all_counts) %>%
               mutate('lfc'=lfc) %>% dplyr::select(lfc, group, n)
  
  
  # Update lfc_counts_all
  lfc_counts_all = lfc_counts_all %>% bind_rows(lfc_counts)
}

# Add missing entries with 0s
lfc_counts_all = expand.grid('group'=unique(lfc_counts_all$group), 'lfc'=unique(lfc_counts_all$lfc)) %>% 
  left_join(lfc_counts_all, by=c('group','lfc')) %>% replace(is.na(.), 0)

# Calculate percentage of each group remaining
tot_counts = DE_info %>% group_by(`gene-score`) %>% tally() %>% filter(`gene-score`!='None') %>%
             mutate('group'=`gene-score`, 'tot'=n) %>% dplyr::select(group, tot) %>%
             bind_rows(data.frame('group'='Neuronal', 'tot'=sum(DE_info$Neuronal)),
                       data.frame('group'='All', 'tot'=nrow(DE_info)))

lfc_counts_all = lfc_counts_all %>% filter(lfc!=-1, group!='None') %>% 
                 left_join(tot_counts, by='group') %>% mutate('perc'=round(100*as.numeric(n)/tot,2))


# Plot change of number of genes
ggplotly(lfc_counts_all %>% ggplot(aes(lfc, perc, color=group)) + geom_point(aes(id=n)) + geom_line() + 
         scale_color_manual(values=SFARI_colour_hue(r=1:8)) + ylab('% of remaining genes') +  xlab('Fold Change') + 
         ggtitle('Effect of filtering thresholds by SFARI score') + theme_minimal())

rm(lfc_list, all_counts, Neuronal_counts, lfc_counts_all, lfc, lfc_counts, lfc_counts_all, tot_counts, lfc_counts_all)

cat(paste0('There are ', sum(DE_info$padj<0.05 & DE_info$`gene-score` != 'None' & !is.na(DE_info$padj)),
           ' SFARI genes that are differentially expressed'))

## There are 381 SFARI genes that are differentially expressed

kable(DE_info %>% filter(padj<0.05 & `gene-score` %in% c(1,2,3) & !is.na(padj)) %>% 
      dplyr::select(ID, `gene-symbol`, log2FoldChange, padj, `gene-score`, Neuronal) %>% arrange(`gene-score`,padj),
      caption = 'Top SFARI scores that are DE')

Top SFARI scores that are DE

ID	gene-symbol	log2FoldChange	padj	gene-score	Neuronal
ENSG00000136535	TBR1	-0.3057313	0.0000952	1	1
ENSG00000136531	SCN2A	-0.1954881	0.0004761	1	1
ENSG00000110066	KMT5B	0.1321022	0.0005438	1	0
ENSG00000197283	SYNGAP1	-0.2212804	0.0016706	1	1
ENSG00000145362	ANK2	-0.1511254	0.0049756	1	1
ENSG00000173575	CHD2	0.1076313	0.0255370	1	0
ENSG00000157540	DYRK1A	-0.0547743	0.0299256	1	0
ENSG00000114166	KAT2B	0.2970078	0.0000004	2	0
ENSG00000108510	MED13	0.1978968	0.0000005	2	0
ENSG00000174469	CNTNAP2	-0.4030458	0.0000025	2	1
ENSG00000055609	KMT2C	0.2222862	0.0000161	2	0
ENSG00000118482	PHF3	0.1827794	0.0000182	2	0
ENSG00000157103	SLC6A1	-0.1884775	0.0000718	2	1
ENSG00000177030	DEAF1	-0.1863424	0.0001451	2	0
ENSG00000155974	GRIP1	-0.2156556	0.0001496	2	1
ENSG00000147050	KDM6A	0.1924217	0.0002788	2	0
ENSG00000061676	NCKAP1	-0.1499081	0.0002889	2	0
ENSG00000038382	TRIO	0.1277617	0.0005954	2	0
ENSG00000196557	CACNA1H	-0.3893469	0.0008310	2	0
ENSG00000257923	CUX1	0.1922092	0.0010873	2	0
ENSG00000123636	BAZ2B	0.2001323	0.0025606	2	0
ENSG00000102786	INTS6	0.1353411	0.0033488	2	0
ENSG00000149930	TAOK2	-0.1307934	0.0037920	2	0
ENSG00000169862	CTNND2	-0.1083342	0.0041750	2	1
ENSG00000125505	MBOAT7	-0.2147595	0.0050990	2	0
ENSG00000112851	ERBIN	0.2117856	0.0058538	2	0
ENSG00000169057	MECP2	-0.1402080	0.0061508	2	1
ENSG00000123066	MED13L	0.1213513	0.0064021	2	0
ENSG00000187555	USP7	-0.1071875	0.0070897	2	0
ENSG00000139613	SMARCC2	-0.1216678	0.0157340	2	0
ENSG00000100354	TNRC6B	0.0743824	0.0176021	2	0
ENSG00000117139	KDM5B	0.0812808	0.0199837	2	0
ENSG00000177565	TBL1XR1	-0.0818411	0.0199909	2	0
ENSG00000119866	BCL11A	-0.1385413	0.0201052	2	1
ENSG00000254585	MAGEL2	-0.9845925	0.0208223	2	0
ENSG00000141027	NCOR1	0.0788144	0.0247581	2	0
ENSG00000079432	CIC	-0.1441292	0.0258571	2	0
ENSG00000103507	BCKDK	-0.1208062	0.0314570	2	0
ENSG00000172264	MACROD2	-0.1059802	0.0326414	2	0
ENSG00000157445	CACNA2D3	-0.1567730	0.0329570	2	0
ENSG00000181722	ZBTB20	0.3504977	0.0000000	3	0
ENSG00000182621	PLCB1	-0.2441636	0.0000000	3	0
ENSG00000141646	SMAD4	0.2217865	0.0000000	3	1
ENSG00000144285	SCN1A	-0.4159570	0.0000000	3	1
ENSG00000168769	TET2	0.2505504	0.0000000	3	0
ENSG00000132294	EFR3A	-0.2174879	0.0000001	3	0
ENSG00000074590	NUAK1	-0.3389639	0.0000001	3	0
ENSG00000196876	SCN8A	-0.2948736	0.0000003	3	1
ENSG00000181090	EHMT1	0.1885501	0.0000003	3	0
ENSG00000170579	DLGAP1	-0.2485110	0.0000006	3	0
ENSG00000078328	RBFOX1	-0.2619988	0.0000013	3	0
ENSG00000175497	DPP10	-0.2165204	0.0000015	3	0
ENSG00000116117	PARD3B	0.3462418	0.0000016	3	0
ENSG00000197535	MYO5A	-0.2531336	0.0000020	3	1
ENSG00000103197	TSC2	-0.1486305	0.0000034	3	1
ENSG00000171759	PAH	-0.7386133	0.0000037	3	0
ENSG00000104388	RAB2A	-0.1404802	0.0000050	3	0
ENSG00000157087	ATP2B2	-0.2951246	0.0000061	3	1
ENSG00000197102	DYNC1H1	-0.1946649	0.0000147	3	0
ENSG00000185345	PRKN	-0.2076752	0.0000156	3	1
ENSG00000021645	NRXN3	-0.2230450	0.0000188	3	0
ENSG00000139726	DENR	-0.1469066	0.0000294	3	0
ENSG00000136854	STXBP1	-0.3440447	0.0000296	3	1
ENSG00000109911	ELP4	0.2517356	0.0000315	3	0
ENSG00000005955	GGNBP2	-0.1529230	0.0000345	3	0
ENSG00000113328	CCNG1	0.2191924	0.0000365	3	1
ENSG00000142230	SAE1	-0.1524692	0.0000423	3	0
ENSG00000083168	KAT6A	0.1295461	0.0000430	3	0
ENSG00000003147	ICA1	-0.1944376	0.0000557	3	0
ENSG00000112655	PTK7	0.3566805	0.0000841	3	1
ENSG00000166313	APBB1	-0.2123724	0.0001062	3	1
ENSG00000124140	SLC12A5	-0.2947685	0.0001509	3	1
ENSG00000050030	NEXMIF	-0.1501763	0.0002959	3	0
ENSG00000135046	ANXA1	0.9375162	0.0003261	3	0
ENSG00000166501	PRKCB	-0.2506441	0.0003264	3	0
ENSG00000079482	OPHN1	0.1904344	0.0003430	3	0
ENSG00000162946	DISC1	0.3168897	0.0003484	3	1
ENSG00000132604	TERF2	-0.1640482	0.0004526	3	0
ENSG00000182771	GRID1	-0.1632933	0.0005041	3	0
ENSG00000087085	ACHE	-0.3947803	0.0005695	3	0
ENSG00000170745	KCNS3	-0.3582251	0.0005768	3	0
ENSG00000146247	PHIP	0.1589965	0.0006217	3	0
ENSG00000164506	STXBP5	-0.1500483	0.0006506	3	0
ENSG00000127914	AKAP9	0.1344363	0.0006929	3	0
ENSG00000146830	GIGYF1	-0.1587235	0.0009162	3	0
ENSG00000128849	CGNL1	0.2631122	0.0011165	3	0
ENSG00000127616	SMARCA4	-0.1637855	0.0012156	3	0
ENSG00000259207	ITGB3	0.7716231	0.0013870	3	0
ENSG00000068793	CYFIP1	0.1981246	0.0014571	3	1
ENSG00000132024	CC2D1A	-0.1399777	0.0017168	3	0
ENSG00000176884	GRIN1	-0.2513044	0.0018130	3	1
ENSG00000205581	HMGN1	0.1797030	0.0018679	3	0
ENSG00000185008	ROBO2	-0.1841714	0.0022998	3	1
ENSG00000072682	P4HA2	0.2345258	0.0024387	3	0
ENSG00000107099	DOCK8	0.3567262	0.0025183	3	0
ENSG00000197724	PHF2	0.1107350	0.0028067	3	0
ENSG00000180370	PAK2	0.1401026	0.0042057	3	0
ENSG00000065526	SPEN	0.1856996	0.0052829	3	0
ENSG00000135439	AGAP2	-0.1856231	0.0053801	3	1
ENSG00000100241	SBF1	-0.1510652	0.0054083	3	0
ENSG00000008083	JARID2	0.1426304	0.0054164	3	0
ENSG00000092964	DPYSL2	-0.1310410	0.0068036	3	1
ENSG00000117362	APH1A	0.1302161	0.0074346	3	0
ENSG00000101489	CELF4	-0.1629384	0.0080235	3	0
ENSG00000107077	KDM4C	0.1402901	0.0092780	3	0
ENSG00000106290	TAF6	-0.1606659	0.0104880	3	0
ENSG00000165300	SLITRK5	-0.1319850	0.0105408	3	0
ENSG00000169918	OTUD7A	-0.1142565	0.0120139	3	0
ENSG00000089006	SNX5	0.1048472	0.0124875	3	0
ENSG00000128815	WDFY4	0.2774197	0.0134723	3	0
ENSG00000183454	GRIN2A	-0.1639773	0.0142114	3	1
ENSG00000095564	BTAF1	0.1347628	0.0153311	3	0
ENSG00000124126	PREX1	0.1300246	0.0154312	3	0
ENSG00000185973	TMLHE	0.1694296	0.0161031	3	0
ENSG00000182256	GABRG3	-0.1314951	0.0201630	3	0
ENSG00000133026	MYH10	-0.1103939	0.0209196	3	1
ENSG00000070190	DAPP1	0.5935422	0.0222045	3	0
ENSG00000104517	UBR5	0.1051990	0.0223542	3	0
ENSG00000139174	PRICKLE1	-0.1278354	0.0263589	3	0
ENSG00000105737	GRIK5	-0.1539730	0.0283319	3	1
ENSG00000075043	KCNQ2	-0.1206093	0.0293157	3	0
ENSG00000181555	SETD2	0.0979522	0.0318025	3	0
ENSG00000145020	AMT	0.1373687	0.0352274	3	0
ENSG00000196361	ELAVL3	-0.1205515	0.0360371	3	0
ENSG00000167632	TRAPPC9	-0.1029582	0.0375666	3	0
ENSG00000184156	KCNQ3	-0.1695747	0.0387824	3	0
ENSG00000134698	AGO4	0.0677230	0.0394623	3	0
ENSG00000009335	UBE3C	-0.0816458	0.0395080	3	0
ENSG00000136425	CIB2	-0.2059795	0.0410004	3	0
ENSG00000135387	CAPRIN1	0.0540932	0.0413076	3	0
ENSG00000174799	CEP135	0.1484550	0.0460509	3	0
ENSG00000108557	RAI1	-0.1395246	0.0465962	3	0

---

Session info

sessionInfo()

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] knitr_1.24                  expss_0.10.1               
##  [3] DESeq2_1.26.0               SummarizedExperiment_1.16.1
##  [5] DelayedArray_0.12.2         BiocParallel_1.20.1        
##  [7] matrixStats_0.55.0          Biobase_2.46.0             
##  [9] GenomicRanges_1.38.0        GenomeInfoDb_1.22.0        
## [11] IRanges_2.20.2              S4Vectors_0.24.3           
## [13] BiocGenerics_0.32.0         ClusterR_1.2.1             
## [15] gtools_3.8.1                Rtsne_0.15                 
## [17] GGally_1.4.0                gridExtra_2.3              
## [19] viridis_0.5.1               viridisLite_0.3.0          
## [21] RColorBrewer_1.1-2          plotlyutils_0.0.0.9000     
## [23] plotly_4.9.2                glue_1.3.1                 
## [25] reshape2_1.4.3              forcats_0.4.0              
## [27] stringr_1.4.0               dplyr_0.8.3                
## [29] purrr_0.3.3                 readr_1.3.1                
## [31] tidyr_1.0.2                 tibble_2.1.3               
## [33] ggplot2_3.2.1               tidyverse_1.3.0            
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.4-1       htmlTable_1.13.1       XVector_0.26.0        
##  [4] base64enc_0.1-3        fs_1.3.1               rstudioapi_0.10       
##  [7] bit64_0.9-7            AnnotationDbi_1.48.0   fansi_0.4.1           
## [10] lubridate_1.7.4        xml2_1.2.2             splines_3.6.0         
## [13] geneplotter_1.64.0     Formula_1.2-3          jsonlite_1.6          
## [16] Cairo_1.5-10           broom_0.5.4            annotate_1.64.0       
## [19] cluster_2.0.8          dbplyr_1.4.2           shiny_1.4.0           
## [22] compiler_3.6.0         httr_1.4.1             backports_1.1.5       
## [25] fastmap_1.0.1          assertthat_0.2.1       Matrix_1.2-17         
## [28] lazyeval_0.2.2         cli_2.0.1              later_1.0.0           
## [31] acepack_1.4.1          htmltools_0.4.0        tools_3.6.0           
## [34] gmp_0.5-13.6           gtable_0.3.0           GenomeInfoDbData_1.2.2
## [37] Rcpp_1.0.3             cellranger_1.1.0       vctrs_0.2.2           
## [40] nlme_3.1-139           crosstalk_1.0.0        xfun_0.8              
## [43] rvest_0.3.5            mime_0.9               lifecycle_0.1.0       
## [46] XML_3.99-0.3           zlibbioc_1.32.0        scales_1.1.0          
## [49] promises_1.1.0         hms_0.5.3              yaml_2.2.0            
## [52] memoise_1.1.0          rpart_4.1-15           RSQLite_2.2.0         
## [55] reshape_0.8.8          latticeExtra_0.6-28    stringi_1.4.6         
## [58] highr_0.8              genefilter_1.68.0      checkmate_1.9.4       
## [61] rlang_0.4.4            pkgconfig_2.0.3        bitops_1.0-6          
## [64] evaluate_0.14          lattice_0.20-38        labeling_0.3          
## [67] htmlwidgets_1.5.1      bit_1.1-15.2           tidyselect_0.2.5      
## [70] plyr_1.8.5             magrittr_1.5           R6_2.4.1              
## [73] generics_0.0.2         Hmisc_4.2-0            DBI_1.1.0             
## [76] pillar_1.4.3           haven_2.2.0            foreign_0.8-71        
## [79] withr_2.1.2            survival_2.44-1.1      RCurl_1.95-4.12       
## [82] nnet_7.3-12            modelr_0.1.5           crayon_1.3.4          
## [85] rmarkdown_1.14         locfit_1.5-9.1         grid_3.6.0            
## [88] readxl_1.3.1           data.table_1.12.8      blob_1.2.1            
## [91] reprex_0.3.0           digest_0.6.24          xtable_1.8-4          
## [94] httpuv_1.5.2           munsell_0.5.0